Generating presentation information associated with one or more objects depicted in image data for display via a graphical user interface

ABSTRACT

A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/644,428, filed Dec. 15, 2021, which is incorporated herein byreference in its entirety.

BACKGROUND

A display of a user device may display a user interface (e.g., agraphical user interface). A user interface may permit interactionsbetween a user of the user device and the user device. In some cases,the user may interact with the user interface to operate and/or controlthe user device to produce a desired result. For example, the user mayinteract with the user interface of the user device to cause the userdevice to perform an action. Additionally, the user interface mayprovide information to the user.

SUMMARY

Some implementations described herein relate to a server device forgenerating presentation information for display via a graphical userinterface (GUI). The server device may include one or more memories andone or more processors communicatively coupled to the one or morememories. The server device may be configured to obtain image data thatdepicts a set of objects associated with a user. The server device maybe configured to process, using at least one image analysis technique,the image data to determine identification information for each objectof the set of objects. The server device may be configured to obtainexchange data related to at least one exchange log of the user. Theserver device may be configured to determine, based on the exchange dataand the identification information, estimated exchange information foreach object of a subset of objects of the set of objects. The serverdevice may be configured to determine, based on the estimated exchangeinformation, estimated assessment information for each object of thesubset of objects. The server device may be configured to generate,based on the estimated assessment information, the presentationinformation for display via the GUI.

Some implementations described herein relate to a non-transitorycomputer-readable medium that stores a set of instructions for a device.The set of instructions, when executed by one or more processors of thedevice, may cause the device to obtain image data that depicts a set ofobjects associated with a user. The set of instructions, when executedby one or more processors of the device, may cause the device to processthe image data to determine identification information for each objectof the set of objects. The set of instructions, when executed by one ormore processors of the device, may cause the device to obtain exchangedata related to at least one exchange log of the user. The set ofinstructions, when executed by one or more processors of the device, maycause the device to determine, based on the exchange data and theidentification information, estimated assessment information for eachobject of a subset of objects of the set of objects. The set ofinstructions, when executed by one or more processors of the device, maycause the device to provide at least some of the estimated assessmentinformation for display via a graphical user interface GUI.

Some implementations described herein relate to a method of generatingpresentation information for display via a GUI. The method may includeobtaining, by a device, image data that depicts an object associatedwith a user. The method may include processing, by the device, the imagedata to determine identification information associated with the object.The method may include obtaining, by the device, exchange data relatedto at least one exchange log of the user. The method may includedetermining, by the device and based on the exchange data and theidentification information associated with the object, estimatedassessment information associated with the object. The method mayinclude generating, by the device and based on the estimated assessmentinformation, the presentation information for display via the GUI.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D are diagrams of an example implementation relating togenerating presentation information associated with one or more objectsdepicted in image data for display via a graphical user interface.

FIG. 2 is a diagram illustrating an example of training and using amachine learning model in connection with generating presentationinformation associated with one or more objects depicted in image datafor display via a graphical user interface.

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3 .

FIG. 5 is a flowchart of an example process relating to generatingpresentation information associated with one or more objects depicted inimage data for display via a graphical user interface.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A person may need to determine an estimated assessment amount (e.g., anestimated value) of one or more objects that the person owns. Forexample, when shopping for an insurance policy (e.g., a homeowner'sinsurance policy or a renter's insurance policy), the person may need todetermine the estimated assessment information to determine an amount ofinsurance coverage that is needed to cover the one or more objects(e.g., as “personal property” or “valuable articles”). In an additionalexample, when submitting an insurance claim for damage to or destructionof the one or more objects, the person may need to determine theestimated assessment information to determine an amount of the insuranceclaim.

In some cases, the person may use a software program or a websiteapplication to itemize and estimate assessment amounts of the one ormore objects. However, this requires an excessive use of computingresources (e.g., processing resources, memory resources, communicationresources, and/or power resources, among other examples) of a userdevice, one or more server devices, or other devices, for the user tointeract with a GUI (e.g., that is presented on the user device via thesoftware program or the website application) for a user to input thisinformation. For example, computing resources are used for the person tonavigate and interact with a large number of pages, fields, menus,and/or other elements to input information that identifies the one ormore objects, that links the one or more objects to exchange information(e.g., receipts of purchase exchanges for the one or more objects), thatestimates assessment values, and/or that provides other informationassociated with the one or more objects. This superfluous navigation andinteraction with a GUI typically creates a poor user experience for theperson as well.

Some implementations described herein provide a device (e.g., a userdevice and/or server device) that obtains image data associated with oneor more images. The one or more images may have been captured by atleast one camera of the user device and may depict scenes associatedwith a user of the user device. For example, the one or more images maydepict “selfie” images of the user, images of friends of the user,and/or images of events or parties attend by the user, among otherexamples. Importantly, the one or more images may depict objects, suchas personal objects of the user that the user purchased. For example, aselfie image of the user may include a depiction of furniture or akitchen appliance that is owned by the user. In some implementations,the device may process the image data to determine identificationinformation associated with a set of objects that are depicted in theimage data. For example, the device may process the selfie imagedescribed above to identify the furniture and/or the kitchen appliance(but not the user).

In some implementations, the device may obtain exchange data related toat least one exchange log of the user (e.g., data that indicates one ormore exchanges, such as purchase exchanges, of the user). Accordingly,the device, based on the identification information associated with theset of objects and the exchange data, may determine estimated assessmentinformation for each object of the set of objects. The estimatedassessment information for each object, of the set of objects, mayindicate an estimated assessment amount (e.g., an estimated presentvalue) of the object. The device then may generate presentationinformation (e.g., based on the estimated assessment information) fordisplay via a GUI (e.g., on a display of the user device). Thepresentation information may include, for each object of the set ofobjects that are associated with the user and that are depicted in theimage data, some or all of the identification information for the objectand/or some or all of the estimated assessment information for theobject. In relation to the example described above, the presentationinformation may identify estimated assessment amounts for the furnitureand the kitchen appliance, respectively.

In this way, some implementations described herein automatically compilean inventory of objects and estimated assessment amounts as presentationinformation and provide the presentation information for display via aGUI. This reduces utilization of computing resources by devices thatwould otherwise be needed to compile the presentation information and/orto present an interface for entering the presentation information (e.g.,as described above in relation to a software program or a websiteapplication). Further, a user experience is improved by automaticallycompiling and providing the presentation information for display via theGUI and thereby minimizing an amount of navigation and/or interaction bythe user to input and/or generate the presentation information.Additionally, the presentation information can be used, for example, toidentify an insurance policy (e.g., a home insurance policy) thatadequately covers objects and/or to automatically prepare an insuranceclaim when the objects have been damaged or destroyed by an insuredevent.

FIGS. 1A-1D are diagrams of an example 100 associated with generatingpresentation information associated with one or more objects depicted inimage data for display via a graphical user interface. As shown in FIGS.1A-1D, example 100 includes a user device, a server device, and a hostserver. These devices are described in more detail in connection withFIGS. 3 and 4 .

As shown in FIG. 1A, and by reference number 105, the user device maycapture one or more images. For example, the user device may include atleast one camera and a user of the user device may interact with theuser device (e.g., via an image capture application of the user device)to cause the at least one camera to capture the one or more images.Accordingly, the user device may store the one or more images in a datastructure that is configured to store images captured by the at leastone camera of the user device, such as an electronic photo album oranother type of image repository. The data structure may be included inthe user device, or may be included in another device that is accessibleto the user device (e.g., via a network), such as the server device.Additionally, or alternatively, the user device may upload the one ormore images to a device (e.g., another server device) associated with anonline service, such as a social media service. For example, the user ofthe user device may interact with the user device (e.g., via an onlineservice application) to log in to an online service account and maycause the user device to upload the one or more images to the deviceassociated with the online service account.

In some implementations, each image, of the one or more images, may beassociated with a scene (e.g., that is within a field of view (FOV) ofthe at least one camera of the user device) at a physical location.Accordingly, the image may depict, for example, one or more people, oneor more objects, one or more animals, and/or one or more structures(e.g., buildings) in the scene at the physical location. In a specificexample, as shown in FIG. 1A, an image may depict two people andmultiple objects, such as a table, a television, and a clock. Over aperiod of time (e.g., a period of hours, days, weeks, months, or years),the user device may capture multiple images of different scenes.Accordingly, image data associated with the multiple images may depictmultiple scenes, multiple people, multiple objects, multiple animals,and/or multiple structures at multiple different instants of time duringthe period of time. In some implementations, the image data may depict aset of objects (e.g., one or more objects of the multiple objects) thatare associated with a particular person, such as the user of the userdevice. For example, the image data may depict a set of objects that areowned by the user.

In some implementations, the user device may include an objectassessment application that is configured to facilitate identifying andassessing objects (e.g., identifying and determining a monetary value ofobjects) depicted in image data. Accordingly, as shown in FIG. 1B, andby reference number 110, the user of the user device may interact withthe user device (e.g., via the object assessment application) to causethe user device to obtain image data. For example, the user device mayidentify the data structure that is configured to store images capturedby the at least one camera of the user device (e.g., the electronicphoto album) and may obtain, from the data structure, image data that isassociated with all of the images stored in the data structure.Alternatively, the user may interact with the user device (via theobject assessment application) to select a set of images (e.g., one ormore images) of all of the images stored in the data structure, whichmay cause the user device to obtain, from the data structure, image datathat is associated with the set of images.

As shown by reference number 115, the user device may send the imagedata (e.g., that was obtained by the user device) to the server device(e.g., to facilitate identifying and assessing objects depicted in theimage data). For example, the user device may send the image data to theserver device by sending the image data to a host server of a network,which may send the image data to the server device. Alternatively, theserver device may obtain the image data from an online service (e.g., asocial media service). For example, the user of the user device mayinteract with the user device (e.g., via the object assessmentapplication) to provide, to the server device, information indicating anonline service account of the user (e.g., information indicating asocial media profile of the user). Accordingly, the server device maycommunicate with a device associated with the online service account(e.g., another server device) to obtain the image data (e.g., todownload image data associated with images posted to the social mediaprofile of the user). In this way, the server device may obtain imagedata that depicts a set of objects (e.g., one or more objects) that areassociated with the user of the user device (e.g., that depicts a set ofobjects that are owned by the user of the user device).

As shown by reference number 120, the server device may determineidentification information for each object of the set of objects thatare associated with the user and that are depicted in the image data.The identification information may indicate for each object, of the setof objects, an identifier of the object (e.g., what the object “is,”such as a table, a television, or a clock), a classification of theobject (e.g., whether the object is “personal property,” a “valuablearticle,” or another type of property under an insurance policy), aproduct name associated with the object (e.g., a manufacturer and/ororiginator of the object), a product model associated with the object(e.g., a particular type of the object), at least one imaging locationassociated with the object (e.g., at least one physical location atwhich at least one image of the object was captured), or at least onetime of imaging of the object (e.g., at least one time at which at leastone image of the object was captured).

In some implementations, the server device may process, using at leastone image analysis technique (e.g., at least one object detectiontechnique, such as a single shot detector (SSD) technique, ayou-only-look-once (YOLO) technique, and/or a recurrent convolutionalneural network (RCNN) technique, among other examples) to determine theidentification information for each object of the set of objects.Additionally, or alternatively, the server device may use a machinelearning model to determine the identification information for eachobject of the set of objects. For example, the user device may process,using the machine learning model, the image data to determine theidentification information for each object of the set of objects. Insome implementations, the user device may train the machine learningmodel based on historical data (e.g., historical image data that depictsobjects) and/or additional information, such as identificationinformation for each of the objects depicted by the historical imagedata. Using the historical data and/or the additional information asinputs to the machine learning model, the server device may train themachine learning model to determine identification information for anobject. In some implementations, the machine learning model may betrained and/or used in a manner similar to that described below withrespect to FIG. 2 .

In some implementations, the server device may process the image data toidentify a subset of the image data. For example, the server device mayprocess the image data to identify a subset of the image data that isassociated with a particular location associated with the user, such aslocation of a home of the user. Accordingly, the server device mayprocess the subset of the image data to determine the identificationinformation for each object of the set of objects. In this way, theserver device may determine identification information for objectsassociated with the particular location, such as objects associated withthe home of the user.

As shown in FIG. 1C, the user may be associated with at least oneexchange log that is stored in data structure that is included in oraccessible to the server device. An exchange log may include one or moreentries that indicate exchanges between the user and another party, suchas a merchant, at one or more particular instants of time. For example,as shown in FIG. 1C, an entry of an exchange log may indicate anexchange identifier (shown as exchange ID) of an exchange, a time of theexchange (shown as Date), an amount associated with the exchange (shownas Amount), and/or a party associated with the exchange (shown asMerchant). In a specific example, as further shown in FIG. 1C, a firstentry of the exchange log indicates that an exchange with an exchangeidentifier of “Exch 1” occurred on “Nov. 1, 2021” for “$19.99” betweenthe user and “Merchant A.” An exchange may be associated with one ormore objects. For example, an exchange may be a purchase, by the user,of the one or more objects at the time of the exchange for the amountassociated with the exchange from the party associated with theexchange. In a specific example, as further shown in FIG. 1C, a secondentry of the exchange log may indicate that the user purchased, in anexchange with an exchange identifier of “Exch 2,” one or more objectsfor $75.00 on “Nov. 12, 2021” from the “Merchant B.” In someimplementations, an exchange log may include exchange information,respectively, for one or more objects. That is, the exchange log mayinclude an entry that indicates an object associated with an exchange,an exchange amount associated with the object, and/or a time of theexchange. For example, the entry may indicate that the user purchased aparticular object (e.g., a television) for a particular amount on aparticular date from a particular party.

As further shown in FIG. 1C, and by reference number 125, the serverdevice may obtain exchange data related to the at least one exchange logof the user. For example, the server device may communicate with theuser device to obtain at least one authentication credential (e.g., ausername and/or password, a security token, and/or at least one othertype of credential) to access the at least one exchange log of the user.Accordingly, the server device may communicate (e.g., using the at leastone authentication credential) with at least one data structure (e.g., adatabase, an electronic folder, and/or an electronic file that storesthe at least one exchange log) to access the at least one exchange logand may thereby obtain the exchange data (e.g., by reading the at leastone exchange log). The exchange data may include and/or indicate theinformation included in the at least one exchange log (e.g., theinformation indicated by the entries of the at least one exchange log).

As shown by reference number 130, the server device may determineestimated exchange information for each object of a subset of objects ofthe set of objects that are associated with the user and that aredepicted in the image data (e.g., based on the exchange data and/or theidentification information). The subset of objects may be objects thatare associated with the exchange data. For example, the subset ofobjects may be objects that were purchased in one or more exchangesincluded in the exchange data. Accordingly, the server device may not(or may be unable to) determine estimated exchange information for otherobjects of the set of objects that are not in the subset of objects(e.g., because the server device has not obtained exchange dataassociated with the other objects).

The estimated exchange information may indicate for each object, of thesubset of objects, an estimated exchange amount associated with theobject and/or an estimated time of exchange associated with the object.For example, the server device may determine, based on theidentification information for the object, at least one parameterassociated with the object, such as an identifier of the object, aclassification of the object, a product name of the object, and/or aproduct model of the object, among other examples. The server device mayidentify, based on the at least one parameter and the exchange data, anexchange event that is associated with the object. For example, theserver device may determine, based on a product name and/or a productmodel of the object (e.g., a pair of shoes associated with a particularcompany brand), an exchange event associated with the object (e.g., apurchase exchange at a store associated with the particular companybrand). The server device may determine, based on the exchange data,exchange event information associated with the exchange event. Forexample, the server device may determine an exchange amount (e.g., apurchase amount) associated with the exchange and a time of theexchange. Accordingly, the server device may determine, based on theexchange event information, the estimated exchange information for theobject. For example, the server device may determine the estimatedexchange amount (e.g., an estimated purchase amount) associated with theobject based on the exchange amount (e.g., the purchase amount)associated with the exchange (e.g., by using one or more exchange amountestimation techniques). As another example, the server device maydetermine the estimated time of exchange associated with the object tobe the time of the exchange indicated by the exchange event information.

As shown by reference number 135, the server device may determineestimated assessment information for each object of the subset ofobjects that are associated with the user and that are depicted in theimage data (e.g., based on the exchange data, the identificationinformation, and/or the estimated exchange information). The estimatedassessment information for each object, of the subset of objects, mayindicate an estimated assessment amount (e.g., an estimated presentvalue) of the object.

In some implementations, the server device may use a machine learningmodel to determine the estimated assessment information for each objectof the subset of objects. For example, the user device may process,using the machine learning model, the estimated exchange information forthe object to determine the estimated assessment information for theobject. As another example, the user device may process, using themachine learning model, the exchange data and/or the identificationinformation to determine the estimated assessment information for theobject. In some implementations, the user device may train the machinelearning model based on historical data (e.g., historical estimatedexchange information, historical exchange data, and/or historicalidentification information for a plurality of objects) and/or additionalinformation, such as estimated assessment information for each of theplurality of objects. Using the historical data and/or the additionalinformation as inputs to the machine learning model, the server devicemay train the machine learning model to determine estimated assessmentinformation for an object. In some implementations, the machine learningmodel may be trained and/or used in a manner similar to that describedbelow with respect to FIG. 2 .

As shown in FIG. 1D, and by reference number 140, the server device maygenerate presentation information (e.g., based on the estimatedassessment information) for display via a GUI. The presentationinformation may include, for each object of the subset of objects thatare associated with the user and that are depicted in the image data,some or all of the identification information for the object and/or someor all of the estimated assessment information for the object. Forexample, as shown in FIG. 1D, the presentation information may includean identifier of the object (shown as Object ID) and the estimatedassessment amount of the object (shown as Amount).

As shown by reference number 145, the server device may provide the GUIto the user device. For example, the server device may send the GUI tothe host server of the network, which may send the GUI to the userdevice. As shown by reference number 150, the user device may display(e.g., when running the object assessment application) the presentationinformation on a display screen of the user device via the GUI.

As further shown in FIG. 1D, and by reference number 155, the userdevice and/or the server device may cause one or more actions to beperformed. For example, one of the user device or the server device maydetermine, based on the estimated assessment information for each objectof the subset of objects, total estimated assessment information for thesubset of objects. The total estimated assessment information mayindicate a sum of respective estimated assessment amounts of the subsetof objects. Accordingly, one of the user device or the server device maygenerate, based on the total estimated assessment information for thesubset of objects, a recommendation, such as an insurance productrecommendation (e.g., to cover the sum of the respective estimatedassessment amounts of the subset of objects), for display via the GUI.Alternatively, one of the user device or the server device may generateand submit, based on the total estimated assessment information for thesubset of objects, a document, such as an insurance claim documentassociated with the subset of objects (e.g., to file an insurance claimfor the sum of the respective estimated assessment amounts of the subsetof objects). For example, one of the user device or the server devicemay generate the insurance claim document and communicate with anotherdevice (e.g., another server device) associated with an insurancecompany to submit the insurance claim document.

As indicated above, FIGS. 1A-1D are provided as an example. In practice,there may be additional devices and/or networks, fewer devices and/ornetworks, different devices and/or networks, or differently arrangeddevices and/or networks than those shown in FIGS. 1A-1D. Furthermore,two or more devices shown in FIGS. 1A-1D may be implemented within asingle device, or a single device shown in FIGS. 1A-1D may beimplemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of one ormore examples 100 may perform one or more functions described as beingperformed by another set of devices of one or more examples 100. Forexample, the user device may perform one or more functions described asbeing performed by the server device, or vice versa.

FIG. 2 is a diagram illustrating an example 200 of training and using amachine learning model in connection with generating presentationinformation associated with one or more objects depicted in image datafor display via a graphical user interface. The machine learning modeltraining and usage described herein may be performed using a machinelearning system. The machine learning system may include or may beincluded in a computing device, a server, a cloud computing environment,or the like, such as the server device and/or the user device describedin more detail elsewhere herein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained from training data (e.g., historical data), such as datagathered during one or more processes described herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from the server device and/or the userdevice, as described elsewhere herein.

As shown by reference number 210, the set of observations includes afeature set. The feature set may include a set of variables, and avariable may be referred to as a feature. A specific observation mayinclude a set of variable values (or feature values) corresponding tothe set of variables. In some implementations, the machine learningsystem may determine variables for a set of observations and/or variablevalues for a specific observation based on input received from theserver device and/or the user device. For example, the machine learningsystem may identify a feature set (e.g., one or more features and/orfeature values) by extracting the feature set from structured data, byperforming natural language processing to extract the feature set fromunstructured data, and/or by receiving input from an operator.

As an example, a feature set for a set of observations may include afirst feature of Exchange Amount, a second feature of Exchange Time, athird feature of Object Info, and so on. As shown, for a firstobservation, the first feature may have a value of $1000, the secondfeature may have a value of Jun. 6, 2015, the third feature may have avalue of LCD TV, and so on. These features and feature values areprovided as examples, and may differ in other examples.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications, orlabels) and/or may represent a variable having a Boolean value. A targetvariable may be associated with a target variable value, and a targetvariable value may be specific to an observation. In example 200, thetarget variable is Assessment Amount, which has a value of $275.00 forthe first observation.

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable. This may bereferred to as an unsupervised learning model. In this case, the machinelearning model may learn patterns from the set of observations withoutlabeling or supervision, and may provide output that indicates suchpatterns, such as by using clustering and/or association to identifyrelated groups of items within the set of observations.

As shown by reference number 220, the machine learning system may traina machine learning model using the set of observations and using one ormore machine learning algorithms, such as a regression algorithm, adecision tree algorithm, a neural network algorithm, a k-nearestneighbor algorithm, a support vector machine algorithm, or the like.After training, the machine learning system may store the machinelearning model as a trained machine learning model 225 to be used toanalyze new observations.

As shown by reference number 230, the machine learning system may applythe trained machine learning model 225 to a new observation, such as byreceiving a new observation and inputting the new observation to thetrained machine learning model 225. As shown, the new observation mayinclude a first feature of $ W, a second feature of X date, a thirdfeature of Y info, and so on, as an example. The machine learning systemmay apply the trained machine learning model 225 to the new observationto generate an output (e.g., a result). The type of output may depend onthe type of machine learning model and/or the type of machine learningtask being performed. For example, the output may include a predictedvalue of a target variable, such as when supervised learning isemployed. Additionally, or alternatively, the output may includeinformation that identifies a cluster to which the new observationbelongs and/or information that indicates a degree of similarity betweenthe new observation and one or more other observations, such as whenunsupervised learning is employed.

As an example, the trained machine learning model 225 may predict avalue of $ Z for the target variable of Assessment Amount for the newobservation, as shown by reference number 235. Based on this prediction,the machine learning system may provide a first recommendation, mayprovide output for determination of a first recommendation, may performa first automated action, and/or may cause a first automated action tobe performed (e.g., by instructing another device to perform theautomated action), among other examples. The first recommendation mayinclude, for example, a recommendation to provide the Assessment Amountto the user device via GUI. The first automated action may include, forexample, generating presentation information that includes theAssessment Amount for display via a GUI.

In some implementations, the trained machine learning model 225 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 240. The observations within a cluster may have athreshold degree of similarity. As an example, if the machine learningsystem classifies the new observation in a first cluster (e.g., aparticular Assessment Amount categorization group), then the machinelearning system may provide a first recommendation, such as the firstrecommendation described above. Additionally, or alternatively, themachine learning system may perform a first automated action and/or maycause a first automated action to be performed (e.g., by instructinganother device to perform the automated action) based on classifying thenew observation in the first cluster, such as the first automated actiondescribed above.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification orcategorization), may be based on whether a target variable valuesatisfies one or more threshold (e.g., whether the target variable valueis greater than a threshold, is less than a threshold, is equal to athreshold, falls within a range of threshold values, or the like),and/or may be based on a cluster in which the new observation isclassified.

In this way, the machine learning system may apply a rigorous andautomated process to determining estimated assessment information for anobject. The machine learning system enables recognition and/oridentification of tens, hundreds, thousands, or millions of featuresand/or feature values for tens, hundreds, thousands, or millions ofobservations, thereby increasing accuracy and consistency and reducingdelay associated with determining estimated assessment information foran object relative to requiring computing resources to be allocated fortens, hundreds, or thousands of operators to manually determineestimated assessment information for an object using the features orfeature values.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2 .

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3 ,environment 300 may include a user device 310, a server device 320, ahost server 330, and a network 340. Devices of environment 300 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

The user device 310 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith generating presentation information for display via a GUI, asdescribed elsewhere herein. The user device 310 may include acommunication device and/or a computing device. For example, the userdevice 310 may include a wireless communication device, a mobile phone,a user equipment, a laptop computer, a tablet computer, a desktopcomputer, a gaming console, a set-top box, a wearable communicationdevice (e.g., a smart wristwatch, a pair of smart eyeglasses, a headmounted display, or a virtual reality headset), or a similar type ofdevice.

The server device 320 includes one or more devices capable of receiving,generating, storing, processing, providing, and/or routing informationassociated with generating presentation information for display via aGUI, as described elsewhere herein. The server device 320 may include acommunication device and/or a computing device. For example, the serverdevice 320 may include a server, such as an application server, a clientserver, a web server, a database server, a host server, a proxy server,a virtual server (e.g., executing on computing hardware), or a server ina cloud computing system. In some implementations, the server device 320includes computing hardware used in a cloud computing environment.

The host server 330 includes one or more devices capable of receiving,generating, storing, processing, providing, and/or routing informationassociated with generating presentation information for display via aGUI, as described elsewhere herein. The host server 330 may include acommunication device and/or a computing device, such as a server device.For example, the host server 330 may include a server, such as anapplication server, a web server, a proxy server, a virtual server(e.g., executing on computing hardware), or a server in a cloudcomputing system. In some implementations, the host server 330 includescomputing hardware used in a cloud computing environment. In someimplementations, the server device 320 is implemented on and integratedwith the host server 330 (e.g., to grant or deny access to resourceshosted or served by the host server 330).

The network 340 includes one or more wired and/or wireless networks. Forexample, the network 340 may include a cellular network, a public landmobile network, a local area network, a wide area network, ametropolitan area network, a telephone network, a private network, theInternet, and/or a combination of these or other types of networks. Thenetwork 340 enables communication among the devices of environment 300.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3 . Furthermore, two or more devices shown in FIG. 3 maybe implemented within a single device, or a single device shown in FIG.3 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400, which maycorrespond to the user device 310, the server device 320, and/or thehost server 330. In some implementations, the user device 310, theserver device 320, and/or the host server 330 may include one or moredevices 400 and/or one or more components of device 400. As shown inFIG. 4 , device 400 may include a bus 410, a processor 420, a memory430, an input component 440, an output component 450, and acommunication component 460.

Bus 410 includes one or more components that enable wired and/orwireless communication among the components of device 400. Bus 410 maycouple together two or more components of FIG. 4 , such as via operativecoupling, communicative coupling, electronic coupling, and/or electriccoupling. Processor 420 includes a central processing unit, a graphicsprocessing unit, a microprocessor, a controller, a microcontroller, adigital signal processor, a field-programmable gate array, anapplication-specific integrated circuit, and/or another type ofprocessing component. Processor 420 is implemented in hardware,firmware, or a combination of hardware and software. In someimplementations, processor 420 includes one or more processors capableof being programmed to perform one or more operations or processesdescribed elsewhere herein.

Memory 430 includes volatile and/or nonvolatile memory. For example,memory 430 may include random access memory (RAM), read only memory(ROM), a hard disk drive, and/or another type of memory (e.g., a flashmemory, a magnetic memory, and/or an optical memory). Memory 430 mayinclude internal memory (e.g., RAM, ROM, or a hard disk drive) and/orremovable memory (e.g., removable via a universal serial busconnection). Memory 430 may be a non-transitory computer-readablemedium. Memory 430 stores information, instructions, and/or software(e.g., one or more software applications) related to the operation ofdevice 400. In some implementations, memory 430 includes one or morememories that are coupled to one or more processors (e.g., processor420), such as via bus 410.

Input component 440 enables device 400 to receive input, such as userinput and/or sensed input. For example, input component 440 may includea touch screen, a keyboard, a keypad, a mouse, a button, a microphone, aswitch, a sensor, a global positioning system sensor, an accelerometer,a gyroscope, and/or an actuator. Output component 450 enables device 400to provide output, such as via a display, a speaker, and/or alight-emitting diode. Communication component 460 enables device 400 tocommunicate with other devices via a wired connection and/or a wirelessconnection. For example, communication component 460 may include areceiver, a transmitter, a transceiver, a modem, a network interfacecard, and/or an antenna.

Device 400 may perform one or more operations or processes describedherein. For example, a non-transitory computer-readable medium (e.g.,memory 430) may store a set of instructions (e.g., one or moreinstructions or code) for execution by processor 420. Processor 420 mayexecute the set of instructions to perform one or more operations orprocesses described herein. In some implementations, execution of theset of instructions, by one or more processors 420, causes the one ormore processors 420 and/or the device 400 to perform one or moreoperations or processes described herein. In some implementations,hardwired circuitry is used instead of or in combination with theinstructions to perform one or more operations or processes describedherein. Additionally, or alternatively, processor 420 may be configuredto perform one or more operations or processes described herein. Thus,implementations described herein are not limited to any specificcombination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 4 are provided asan example. Device 400 may include additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 4 . Additionally, or alternatively, a set ofcomponents (e.g., one or more components) of device 400 may perform oneor more functions described as being performed by another set ofcomponents of device 400.

FIG. 5 is a flowchart of an example process 500 associated withgenerating presentation information associated with one or more objectsdepicted in image data for display via a graphical user interface. Insome implementations, one or more process blocks of FIG. 5 may beperformed by a device (e.g., the user device 310 or the server device320). In some implementations, one or more process blocks of FIG. 5 maybe performed by another device or a group of devices separate from orincluding the device. Additionally, or alternatively, one or moreprocess blocks of FIG. 5 may be performed by one or more components ofdevice 400, such as processor 420, memory 430, input component 440,output component 450, and/or communication component 460.

As shown in FIG. 5 , process 500 may include obtaining image data thatdepicts a set of objects associated with a user (block 510). As furthershown in FIG. 5 , process 500 may include processing, using at least oneimage analysis technique, the image data to determine identificationinformation for each object of the set of objects (block 520). Asfurther shown in FIG. 5 , process 500 may include obtaining exchangedata related to at least one exchange log of the user (block 530). Asfurther shown in FIG. 5 , process 500 may include determining estimatedexchange information for each object of a subset of objects of the setof objects (block 540). As further shown in FIG. 5 , process 500 mayinclude determining estimated assessment information for each object ofthe subset of objects (block 550). As further shown in FIG. 5 , process500 may include generating, based on the estimated assessmentinformation, the presentation information for display via the GUI (block560).

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise forms disclosed. Modifications may be made in light of the abovedisclosure or may be acquired from practice of the implementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software. Itwill be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

Although particular combinations of features are recited in the claimsand/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set. As used herein, aphrase referring to “at least one of a list of items refers to anycombination of those items, including single members. As an example, “atleast one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c,and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, or a combination of related and unrelateditems), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A device, comprising: one or more memories; andone or more processors, coupled to the one or more memories, configuredto: determine estimated exchange information for an object; process theestimated exchange information to determine estimated assessmentinformation for the object based on determining a threshold degree ofsimilarity for the estimated assessment information to a particularassessment amount categorization group; and generate presentationinformation for display via a graphical user interface based on thesimilarity of the estimated assessment information to the particularassessment amount categorization group.
 2. The device of claim 1,wherein the estimated exchange information for the object indicates anestimated time of exchange associated with the object.
 3. The device ofclaim 1, wherein the estimated exchange information for the objectindicates an estimated exchange amount associated with the object. 4.The device of claim 1, wherein the one or more processors are furtherconfigured to: obtain image data for the object from a user device; andwherein the one or more processors configured to determine the estimatedexchange information for the object are configured to: determine theestimated exchange information for the object based on the image data.5. The device of claim 1, wherein the one or more processors, whendetermining the estimated exchange information for the object, areconfigured to: identify an exchange event that is associated with theobject; determine exchange event information associated with theexchange event; and determine, based on the exchange event information,the estimated exchange information for the object.
 6. The device ofclaim 1, wherein the one or more processors are further configured to:process, using an image analysis technique, image data that depicts asubject of the object to determine identification information for theobject; and wherein the one or more processors configured to determinethe estimated exchange information for the object are configured to:determine the estimated exchange information for the object based on theidentification information.
 7. The device of claim 1, wherein the one ormore processors are further configured to: generate, based on theestimated assessment information, a recommendation for display via thegraphical user interface.
 8. A method, comprising: determining, by adevice, estimated exchange information for an object of a subset ofobjects; determining, by the device and based on the estimated exchangeinformation, estimated assessment information for the object based on athreshold degree of similarity of the estimated assessment informationto a particular assessment amount categorization group; and generating,by the device, presentation information for display via a graphical userinterface based on the degree of similarity of the estimated assessmentinformation to the particular assessment amount categorization group. 9.The method of claim 8, wherein determining the estimated exchangeinformation for the object comprises: determining the estimated exchangeinformation based on image data.
 10. The method of claim 8, whereindetermining the estimated assessment information for the objectcomprises: determining the estimated assessment information for theobject based on a machine learning model.
 11. The method of claim 8,wherein the estimated exchange information indicates an estimatedexchange amount associated with the object and an estimated time ofexchange associated with the object.
 12. The method of claim 8, whereinthe estimated exchange information is based on one or more of: a productname of the object, or a product model of the object.
 13. The method ofclaim 8, further comprising: processing, using an image analysistechnique, image data that depicts a subject of the object to determineidentification information for the object; and wherein determining theestimated exchange information for the object comprises: determining theestimated exchange information for the object based on theidentification information.
 14. The method of claim 8, furthercomprising: generating, based on the estimated assessment informationfor the object, a recommendation for display via the graphical userinterface.
 15. A non-transitory computer-readable medium storinginstructions, the instructions comprising: one or more instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: determine estimated exchange information for an object;determine estimated assessment information for the object based on athreshold degree of similarity of the estimated assessment informationto a particular cluster, wherein the particular cluster includes theobject and one or more related objects; and generate presentationinformation for display based on the similarity of the estimatedassessment information to the particular cluster.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the estimated exchangeinformation is based on one or more of: a product name of the object, ora product model of the object.
 17. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions, when executedby the one or more processors, further cause the one or more processorsto: obtain exchange data related to an exchange log; and determine,based on the exchange data, the estimated exchange information for theobject.
 18. The non-transitory computer-readable medium of claim 15,wherein the one or more instructions, when executed by the one or moreprocessors, further cause the one or more processors to: generate, basedon the estimated assessment information, a recommendation for displayvia a graphical user interface.
 19. The non-transitory computer-readablemedium of claim 15, wherein the one or more instructions, when executedby the one or more processors, further cause the one or more processorsto: obtain image data for the object from a user device; and wherein theone or more instructions, that cause the one or more processors todetermine the estimated exchange information for the object, cause theone or more processors to: determine the estimated exchange informationfor the object based on the image data.
 20. The non-transitorycomputer-readable medium of claim 19, wherein identification informationassociated with the image data for the object indicates at least one of:an identifier of the object, a classification of the object, a productname associated with the object, a product model associated the object,or at least one imaging location associated with the object.